Many reports show that dominant frequency of high frequency component(HF:0.15∼0.4Hz) of heart rate time series is synchronized with the respiratory frequency. In this paper, we proposed the method for estimating the condition of respiration continuously using dominant frequency and power of HF (HFP) of heart rate time series. Dominant frequency and HFP is calculated from the interval of the neighboring two extreme points and the subtraction of the neighboring two extremals.In the experimental results, high frequency components did not disappear completely during breath-holding. This fact is different from the previous study. Subjects were classified into two groups. In one group, dominant frequency of the HF is significant increased during breath-holding compared with normal breathing. In the other group, this phenomenon was not observed. On the other hand, HFP of total subjects significantly decreased during breath-holding compared with normal breathing. Correct rate during breath-holding and error rate during rest and recovery are calculated using HFP. In the results, average and S. D. of correct rate during breath-holding were 65.0±26.3%. Correct rate of 18 subjects was 80.0±14.1% and correct rate of other 8 subjects was 31.5±11.9%. Our method is expected to apply to the development of the respiratory monitor which can measure respiratory condition with non-restricted in continuously.
Regeneration of the central nervous system (CNS) is one of the most important research themes in neuroscience and neuroengineering. It is essential to replenish the lost neurons and to establish appropriate functional neuronal networks using pluripotent stem cells. Little is known, however, about the properties of stem cell-derived neuronal networks, particularly under the differentiation and development processes. In this work, we cultured P19 embryonal carcinoma cells on micro-electrode arrays (MEAs). P19 cells were differentiated into neurons by retinoic acid application and formed densely connected networks. Spontaneous electrical activity was extracellulary recorded through substrate electrodes and analyzed. Synchronized periodic bursts, which were the characteristic features in primary cultured CNS neurons, were observed. Pharmacological studies demonstrated that the glutamatergic excitatory synapses and the GABAergic inhibitory synapses were active in these P19-derived neuronal networks. The results suggested that MEA-based recording was useful for monitoring differentiation processes of stem cells. P19-derived neuronal networks had quite similar network properties to those of primary cultured neurons, and thus provide a novel model system to investigate stem cell-based neuronal regeneration.
This paper introduces a model for extracting features of an electroencephalogram (EEG) and a method for evaluating the model. In general, it is known that an EEG contains personal features. However, extraction of these personal features has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the EEG feature as a first factor loading, which is calculated by eigenvalue decomposition. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and extracted EEG features. In general, the distance metric used is Euclidean distance. We believe that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance, and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform a computer simulation using real EEG data.
Microelectrode arrays are commonly used to measure neural activities in the brain, and arrays with some 100 electrodes are commercially available to date. However, insertion of a dense grid array deforms the brain, resulting in deterioration of the measurements. In order to overcome this problem, we propose a piezo-driven vibrating insertion device to reduce the insertion-induced deformation of the brain. We attempted under various conditions to insert the array into an agarose substrate, whose hardness was adjusted to that of the cerebral cortex of rats. Our experiments demonstrated that inverse-sawtooth vibration reduced the insertion-induced deformation of the substrate in proportion to the logarithm of an upstroke velocity when the velocity was higher than 10 mm/s, and vibrating insertion of the maximum velocity at 36.7 mm/s reduced the deformation by up to 40% as compared to insertion without vibration. In addition, we tested the vibrating insertion device in an electrophysiological experiment in the rat auditory cortex in vivo, and successfully measured tone-evoked neuronal activities.
The fundamental frameworks for possessing qualia are “embodiment” and the network structures of the relationships between internal modules. We proposed “Anaplastic cognitive agent (ACA)” composed by interactions between sub modules with hierarchical history functions and network structures. A dissociated culture system can discriminate several distinct spatiotemporal patterns of action potentials evoked by current inputs, and possesses kinds of history function; a history properties of network dynamics, synaptic plasticity, and so on. These features are fundamental for parts to compose ACA.
Recently, high-speed communication network, e.g., the Internet, has seen rapid development. Therefore, the construction of control systems employing these networks is becoming a real possibility. The realization of such systems, however, has the following two limitations. i) bit rate limitation of network, and ii) network's reliability, e.g., delays, data dropouts, and so on. This paper uses dynamic quantization in order to overcome the first problem. For the second problem, an data-driven zero-order hold is used to supress the effects of stochastic data dropout through an unreliable network. The stochastic stability of the system is analyzed and its sufficient condition is developed in this paper. Finally, the validity of the proposed method is demonstrated through some experiments.
In this paper, an optimal design method for stable IIR(Infinite Impulse Response) filters in a criterion of min-max sense is proposed. The design problem is considered one of the complex Chebyshev approximation for rational function including the stability constraint, we formulated such the problem as a real linear semi-infinite programming using the real rotation theorem. Then, the problem is solved by the three phase method that is one of the methods solving semi-infinite programming problem. The three phase method is composed of three operations. In the first operation, some candidates of active constraints are selected by the iterative simplex method. Next, the second operation integrates some degenerate constraints. In the third operation, the approximation solution obtained until second operation is adjusted so as to satisfy the optimality condition. As a result, the filters designed by the method are more precise than one designed by conventional method. Several design examples are shown to present effectiveness of the proposed method.
In a hardware implementation of FIR(Finite Impulse Response) digital filters, it is desired to reduce a total number of nonzero digits used for a representation of filter coefficients. In general, a design problem of FIR filters with CSD(Canonic Signed Digit) representation, which is efficient one for the reduction of numbers of multiplier units, is often considered as one of the 0-1 combinational problems. In such the problem, some difficult constraints make us prevent to linearize the problem. Although many kinds of heuristic approaches have been applied to solve the problem, the solution obtained by such a manner could not guarantee its optimality. In this paper, we attempt to formulate the design problem as the 0-1 mixed integer linear programming problem and solve it by using the branch and bound technique, which is a powerful method for solving integer programming problem. Several design examples are shown to present an efficient performance of the proposed method.
In this paper, we describe the brain activity associated with kanji characters expressing emotion, which are places at the end of a sentence. Japanese people use a special kanji character in brackets at the end of sentences in text messages such as those sent through e-mail and messenger tools. Such kanji characters plays a role to expresses the sender's emotion (such as fun, laughter, sadness, tears), like emoticons. It is a very simple and effective way to convey the senders' emotions and his/her thoughts to the receiver. In this research, we investigate the effects of emotional kanji characters by using an fMRI study. The experimental results show that both the right and left inferior frontal gyrus, which have been implicated on verbal and nonverbal information, were activated. We found that we detect a sentence with an emotional kanji character as the verbal and nonverval information, and a sentence with emotional kanji characters enrich communication between the sender and the reciever.
In this paper, we investigate optimized quantization method in JPEG2000 application for medical ultrasonic echo images. JPEG2000 has been issued as the new standard for image compression technique, which is based on Wavelet Transform (WT) and JPEG2000 incorporated into DICOM (Digital Imaging and Communications in Medicine). There are two quantization methods. One is the scalar derived quantization (SDQ), which is usually used in standard JPEG2000. The other is the scalar expounded quantization (SEQ), which can be optimized by user. Therefore, this paper is an optimization of quantization step, which is determined by Genetic Algorithm (GA). Then, the results are compared with SDQ and SEQ determined by arithmetic average method. The purpose of this paper is to improve image quality and compression ratio for medical ultrasonic echo images. The image quality is evaluated by objective assessment, PSNR (Peak Signal to Noise Ratio) and subjective assessment is evaluated by ultrasonographers from Tokai University Hospital and Tokai University Hachioji Hospital. The results show that SEQ determined by GA provides better image quality than SDQ and SEQ determined by arithmetic average method. Additionally, three optimization methods of quantization step apply to thin wire target image for analysis of point spread function.
This paper presents a novel method for moving sound source localization and its performance evaluation in actual room environments. The method is based on the MUSIC (MUltiple SIgnal Classification) which is one of the most high resolution localization methods. When using the MUSIC, a computation of eigenvectors of correlation matrix is required for the estimation. It needs often a high computational costs. Especially, in the situation of moving source, it becomes a crucial drawback because the estimation must be conducted at every the observation time. Moreover, since the correlation matrix varies its characteristics due to the spatial-temporal non-stationarity, the matrix have to be estimated using only a few observed samples. It makes the estimation accuracy degraded. In this paper, the PAST (Projection Approximation Subspace Tracking) is applied for sequentially estimating the eigenvectors spanning the subspace. In the PAST, the eigen-decomposition is not required, and therefore it is possible to reduce the computational costs. Several experimental results in the actual room environments are shown to present the superior performance of the proposed method.
In this paper, we present a realtime implementation of target sound extraction operating in a frequency domain using microphone array and its performance evaluation in actual room environments. In the realized system, the target sound extraction can be achieved by using the FDGSC (Frequency Domain Generalized Sidelobe Canceller) which is one of GSCs. At each the frequency band, the FDGSC is composed of a fixed beamformer part for target sound enhancement and an adaptive filter part for noisy sound reduction. Since the FDGSC is required to perform at every sound data block to apply the DFT (Discrete Fourier Transform), a processing for a series of block data must be completed within a transfer time of block data in the realtime system. Then, we implement the FDGSC using a general purpose DSP (Digital Signal Processor) evaluation board which enable us to perform a high speed signal processing. A superior performance of realtime processing and target sound extraction are shown by several experimental results in the actual room environments.
This paper proposes two new similarity measures for the content-based image retrieval (CBIR) systems. The similarity measures are based on the k-means clustering algorithm and the multidimensional generalization of the Wald-Wolfowitz (MWW) runs test. The performance comparisons between the proposed similarity measures and a current CBIR similarity measure based on the MWW runs test were performed, and it can be seen that the proposed similarity measures outperform the current similarity measure with respect to the precision and the computational time.
Image-based 3D reconstruction is a useful and active research area. However, it is a challenge to compute 3D measurements in real-time for high resolution input images even if special hardwares are used. This paper proposes a new coarse-to-fine method that can reduce the computation time of the stereo matching problem. The time reduction is done by sampling disparity spaces and computing the matching costs at only the sampled positions. The disparity map that is derived from a sampled disparity space is used to limit the search region for the finer map to its surrounding region. Because of the sampling of disparity spaces and the limitation of the search region, the computation time is reduced dramatically even if the disparity search range is enlarged significantly. The proposed method has been tested with several public stereo image datasets on the internet. The experimental results indicate that the proposed method can save much of the computation time compared to the other methods that need to compute all of matching costs inside disparity spaces.
This paper presents a new algorithm for incremental learning, which is named Incremental Simple-PCA. This algorithm adds an incremental learning function to the Simple-PCA that is an approximation algorithm of the principal component analysis where an eigenvector can be calculated by a simple repeated calculation. Using the proposed algorithm, it is possible to update the eigenvector faster by using incremental data. We carry out computer simulations on personal authentication that uses face images and wrist motion recognition that uses wrist EMG by incremental learning to verify the effectiveness of this algorithm. These results were compared with the results of Incremental PCA that introduced incremental learning function to the conventional PCA.
In this paper, we consider the automatic text classification as a series of information processing, and propose a new classification technique, namely, “Frequency Ratio Accumulation Method (FRAM)”. This is a simple technique that calculates the sum of ratios of term frequency in each category. However, it has a desirable property that feature terms can be used without their extraction procedure. Then, we use “character N-gram” and “word N-gram” as feature terms by using this property of our classification technique. Next, we evaluate our technique by some experiments. In our experiments, we classify the newspaper articles of Japanese “CD-Mainichi 2002” and English “Reuters-21578” using the Naive Bayes (baseline method) and the proposed method. As the result, we show that the classification accuracy of the proposed method improves greatly compared with the baseline. That is, it is 89.6% for Mainichi, 87.8% for Reuters. Thus, the proposed method has a very high performance. Though the proposed method is a simple technique, it has a new viewpoint, a high potential and is language-independent, so it can be expected the development in the future.
Recommender systems have become an important research area as they provide some kind of intelligent web techniques to search through the enormous volume of information available on the internet. Content-based filtering and collaborative filtering methods are the most widely recommendation techniques adopted to date. Each of them has both advantages and disadvantages in providing high quality recommendations therefore a hybrid recommendation mechanism incorporating components from both of these methods would yield satisfactory results in many situations. In this paper, we present an elegant and effective framework for combining content-based filtering and collaborative filtering methods. Our approach clusters on user information and item information for content-based filtering to enhance existing user data and item data. Based on the result from the first step, we calculate the predicted rating data for collaborative filtering. We then do cluster on predicted rating data in the last step to enhance the scalability of our proposed system. We call our proposal multi-based clustering method. We show that our proposed system can solve a cold start problem, a sparsity problem, suitable for various situations in real-life applications. It thus contributes to the improvement of prediction quality of a hybrid recommender system as shown in the experimental results.
In this paper, we propose an approach of constructing the knowledge base for “Analects of Confucius”, which aims to help the correct understanding of “Analects of Confucius”. The content of “Analects of Confucius” has the characteristics that it has not been categorized by topics and always has comprehensive meanings. Thus it is necessary to build an framework to manage and build the knowledge base for it. The construction of knowledge base in the past work has been focusing on the research of the words and the shallow meaning and explicitly communicated meaning of the passages which can not be used for deeper meaning and implicitly communicated meaning. The present paper sets up an categorization system for “Analects of Confucius”, then based on it, the knowledge base is built by using pragmatics information with reference to utterance interpretation method in pragmatics. The question answering system adopts the knowledge base of “Analects of Confucius” and gives an assessment of it. The results show that the combined answer with pragmatics information and modern text interpretation outperforms the answers which are extracted only from modern text by 32.2%.
A new autonomous implant sensor positioning measurement technique is discussed. The goal of this study is to develop ubiquitous medical systems with autonomous nano-medical sensors and robots. The key for the goal is to establish the autonomous positioning measurement for these implant objects without the burden of patients. To achieve the goal, this study proposes a autonomous positioning measurement with human voxel data. The technique is combining two algorithms; an autonomous distributed measurement of sensors with Sammon's Map which is a nonlinear mapping algorithm and voxel human model clustered with the propagation characteristic of human tissue. The simulation experiment shows the proposed technique enables precise positioning measurement under heterogeneous propagation environment in human body.
We proposed an unequal error protection (UEP) scheme using trellis coded modulation and an adaptive equalizer for use in mobile fading channel communication environments. We proposed a signal constellation to realize unequal error protection and showed its performance using computer simulations.
In this paper, we propose a musical whistling test system. This system consists of two parts. One is a blow/draw notes detection part using air-transmitted sounds. And the other is a pitch detection part using bone-transmitted sounds. This paper shows that the proposed system using air-transmitted sounds and bone-transmitted sounds gives good performance.
This paper presents a new control circuit to create high-performance non-inverted Buck-Boost converter with dual ⊿∑ modulations. Experimental load regulation, corresponding to load current steps of ±0.5A, is within 45mVpp, and the efficiency without synchronized rectifier is 83% at input voltage 2.5V and load current 0.8A.
In this paper, we propose a new adaptive noise canceller using a linear phase filter. The linear model in which the arbitrary signal is defined by the output signal of a linear system at the white signal input is used. The noise is suppressed by the estimated linear system and the signal to noise ratio is improved. At this time, to minimize the distortion of the signal due to the nonlinearity of the phase shift, the linear phase filter has been newly introduced. The transfer function of the linear system is an arbitrary minimum phase rational transfer function that has the poles and zeros. It has the feature of not being specified for all pole model. The adaptive algorithm is a gradient based algorithm with few computational complexities. The feature of the proposed adaptive noise canceller are that the inverse filter of the adaptive filter is stable, the convergence of the algorithm is guaranteed, the distortion of the signal is minimum , and there is no restriction to the transfer function of the linear system.
In this paper a design of the decoupling control of multi-input multi-output (MIMO) linear system is discussed. A new configuration of the prepositional tandem matrix is shown as a decoupling compensator, and the minimum-phase state control is applied to the resulting decoupled system. In general non minimum-phase characteristics is often accompanied to the decoupled systems. The feedforward compensation makes the non minimum-phase effect of each decoupled scalar system change to delay time. A numerical example is given for the MIMO linear system which conventionally results in non minimum-phase systems.
In this paper, for the design of delay time control systems a notice is taken on the minimum-phase state of the controlled object. By the feedback on the observed minimum-phase state, the control system can be stabilized diminishing the effect of delay times. And feed forward compensation is expected on a standpoint to cancel delay times in the input-output characteristics. For the delay time control systems the decoupling control is necessary, and the design of decoupling tandem compensator with the minimum degree is shown. The feedback on the observed minimum-phase state and the feed forward compensation are also useful for delay time decoupling control systems. A numerical example for the 2input 2output controlled object with delay time characteristics is shown and the decoupling control with the feedforward compensation is designed.
We report on cooperative control of multiple neural networks for indoor balloon robot. In our laboratory, the indoor balloon has been studied to achieve various applications. Our objective of this paper is to propose a robust controller that can adapt to mechanical accidents such as breakdown of propellers. In our proposal method, each propeller thrust is independently calculated by each small network. We confirm effectiveness of the proposed method compared to the method of calculating thrusts by a single neural network on the simulator.
Generally, it is difficult to simulate crosstalk due to complicated reflected waves caused by the capacitive nonlinear load. In this letter, a simulation method for the reflected wave and the crosstalk using the V-Q characteristic of the load is described.