Akira Iwata

Nagoya Institute of Technology, Nagoya, Aichi, Japan

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Publications (98)18.74 Total impact

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    ABSTRACT: Sound localization is an important ability intrinsic to animals, being currently explored by several researches. Even though several systems and implementations have being proposed, the majority is very complex and not suitable for embedded systems. This paper proposes a new approach for binaural sound localization and the corresponding implementation in an Field Programable Gate Array (FPGA) device. The system is based on the signal processing modules of a previously proposed sound processing system, which converts the input signal to spike trains. The time difference extraction and feature generation methods introduced in this paper create simple binary feature vectors, used as training data for a standard LVQ neural network. An output temporal layer uses the time information of the sound signals in order to reduce the misclassifications of the classifier. Preliminary experimental results show high accuracy with small logic and memory requirements.
    Artificial Neural Networks - ICANN 2010 - 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I; 01/2010
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    ABSTRACT: Many applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. However, one of the main difficulties of such kind of system is the natural presence of mixed sound sources in real environments. This paper proposes a pulsed neural network based system for extraction and recognition of objective sound sources from background sound source. The system uses the short term depression, that implements by the weight’s decay in the output layer and changing the weight by frequency component in the competitive learning network. Experimental results show that objective sounds could be successfully extracted and recognized.
    Neural Information Processing. Theory and Algorithms - 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part I; 01/2010
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    ABSTRACT: The detection of approaching vehicles is a very important topic on the development of complementary traffic safety systems. However, the majority of the proposed approaches are very complex and not suitable for embedded applications. This paper proposes a new sound approaching detection algorithm specifically intended for hardware implementation. Experimental results show higher accuracy and earlier detection when comparing to other methods.
    Neural Information Processing. Models and Applications - 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part II; 01/2010
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    ABSTRACT: Several applications would emerge from the development of efficient and robust sound classification systems able to identify the nature of non-speech sound sources. This paper proposes a novel approach that combines a simple feature generation procedure, a supervised learning process and fewer parameters in order to obtain an efficient sound classification system solution in hardware. The system is based on the signal processing modules of a previously proposed sound processing system, which convert the input signal in spike trains. The feature generation method creates simple binary features vectors, used as the training data of a standard LVQ neural network. An output temporal layer uses the time information of the sound signals in order to eliminate the misclassifications of the classifier. The result is a robust, hardware friendly model for sound classification, presenting high accuracy for the eight sound source signals used on the experiments, while requiring small FPGA logic and memory resources.
    Advances in Neuro-Information Processing, 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II; 01/2008
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    ABSTRACT: Modern applications of pattern recognition generate very large amounts of data, which require large computational effort to process. However, the majority of the methods intended for large-scale problems aim to merely adapt standard classification methods without considering if those algorithms are appropriated for large-scale problems. CombNET-II was one of the first methods specifically proposed for such kind of a task. Recently, an extension of this model, named CombNET-III, was proposed. The main modifications over the previous model was the substitution of the expert networks by Support Vectors Machines (SVM) and the development of a general probabilistic framework. Although the previous model's performance and flexibility were improved, the low accuracy of the gating network was still compromising CombNET-III's classification results. In addition, due to the use of SVM based experts, the computational complexity is higher than CombNET-II. This paper proposes a new two-layered gating network structure that reduces the compromise between number of clusters and accuracy, increasing the model's performance with only a small complexity increase. This high-accuracy gating network also enables the removal the low confidence expert networks from the decoding procedure. This, in addition to a new faster strategy for calculating multiclass SVM outputs significantly reduced the computational complexity. Experimental results of problems with large number of categories show that the proposed model outperforms the original CombNET-III, while presenting a computational complexity more than one order of magnitude smaller. Moreover, when applied to a database with a large number of samples, it outperformed all compared methods, confirming the proposed model's flexibility.
    01/2008;
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    ABSTRACT: Pulsed Neuron (PN) model was proposed as one of the simplest models working by pulse trains. PN model has a membrane potential to deal with the temporal information, and the calculation process is inexpensive. However, as the output function of PN model is an Unit Step function, PN model cannot directly use the back-propagation (BP) method. It would be possible to solve general pattern recognition problems if the PN model could be trained by the BP method. In this paper, we propose a BP method for multilayer pulsed neural networks. The proposed method uses the duality of PN model, in which the desired output of hidden layer neuron is calculated from output layer neurons’ weights and output. Experimental results show that the multilayer pulsed neural networks can learn and recognize non-linear problems using the proposed method.
    Advances in Neuro-Information Processing, 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II; 01/2008
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    ABSTRACT: Pulsed neurons are suitable for processing time series data, like sound signals, and can be easily implemented in hardware. In this paper, we propose an aural information processing system based on the human auditory system using a pulsed neuron model and a correspondent implementation in an FPGA device. Experimental results show that an FPGA based implementation of the proposed system can successfully identify the results faster than a similar software implementation. Noise tolerance experimental results are also presented.
    Neural Networks, 2007. IJCNN 2007. International Joint Conference on; 09/2007
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    ABSTRACT: Chronic hepatitis C is a disease that is difficult to treat. At present, interferon might be the only drug, which can cure this kind of disease, but its efficacy is limited and patients face the risk of side effects and high expense, so doctors considering interferon must make a serious choice. The purpose of this study is to establish a simple model and use the clinical data to predict the interferon efficacy. This model is a combination of Feature Subset Selection and the Classifier using a Support Vector Machine (SVM). The study indicates that when five features have been selected, the identification by the SVM is as follows: the identification rate for the effective group is 85%, and the ineffective group 83%. Analysis of selected features show that HCV-RNA level, hepatobiopsy, HCV genotype, ALP and CHE are the most significant features. The results thus serve for the doctors' reference when they make decisions regarding interferon treatment.
    Journal of Medical Systems 05/2007; 31(2):117-23. · 1.78 Impact Factor
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    ABSTRACT: The X-ray CT has a high resolution with respect to the absorption coefficient, while its spatial resolution is inferior to other X-ray imaging techniques. Consequently, the improvement of its spatial resolution is strongly desired. This paper theoretically analyzes the frequency component of the projection data, obtained by the third-generation CT system using a wide-angle fan beam. It is shown that the projection data contain the effective frequency component above the Nyquist frequency determined by the spacing between detectors. Then a new algorithm is proposed which utilizes that effective frequency component. The usefulness of the algorithm is examined by an experiment using a phantom and the human body. It is shown as a result that the new algorithm can realize spatial resolution corresponding to approximately twice the Nyquist frequency determined by the detector spacing. Using this algorithm, the spatial resolution of the CT image can be improved without decreasing the detector spacing.
    Systems and Computers in Japan 03/2007; 17(7):19 - 29.
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    ABSTRACT: Several applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. This paper proposes an integrated sound localization and classification system based on the human auditory system and a respective compact hardware implementation. The proposed models are based on spiking neurons, which are suitable for processing time series data, like sound signals, and can be easily implemented in hardware. The system uses two microphones, extracting the time difference between the two channels with a chain of coincidence detection spiking neurons. A spiking neural networks process the time-delay pattern, giving a single directional output. Simultaneously, an independent spiking neural network process the spectral information of on audio channel in order to classify the source. Experimental results show that a the proposed system could successfully locate and identify several sound sources in real time with high accuracy.
    Neural Information Processing, 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II; 01/2007
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    ABSTRACT: Current automobiles’ safety systems based on video cameras and movement sensors fail when objects are out of the line of sight. This paper proposes a system based on pulsed neural networks able to detect if a sound source is approaching a microphone or moving away from it. The system, based on PN models, compares the sound level difference between consecutive instants of time in order to determine its relative movement. Moreover, the combined level difference information of all frequency channels permits to identify the type of the sound source. Experimental results show that, for three different vehicles sounds, the relative movement and the sound source type could be successfully identified.
    Neural Information Processing, 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I; 01/2007
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    ABSTRACT: Many applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. However, one of the main difficulties of such kind of system is the natural presence of multiple sound sources in real environments. This paper proposes a pulsed neural network based system for separation and recognition of multiple sound sources based on the difference on time lag of the different sources. The system uses two microphones, extracting the time difference between the two channels with a chain of coincidence detection pulsed neurons. An unsupervised neural network processes the firing information corresponding to each time lag in order to recognize the type of the sound source. Experimental results show that three simultaneous musical instruments’ sounds could be successfully separated and recognized.
    Artificial Neural Networks - ICANN 2007, 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II; 01/2007
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    ABSTRACT: Although liver biopsy is currently regarded as the gold standard for staging liver fibrosis in chronic hepatitis C, it is a costly invasive procedure and carries a small risk for complication. Our aim in this study was to construct a simple model to distinguish between patients with no or mild fibrosis (METAVIR F0-F1) versus those with clinically significant fibrosis (METAVIR F2-F4). We retrospectively studied 204 consecutive CHC patients. Thirty-four serum markers with age, gender, duration of infection were assessed to classify fibrosis with a classifier known as the support vector machine (SVM). The method of feature selection known as sequential forward floating selection (SFFS) was introduced before the performance of SVM. When four serum markers were extracted with SFFS-SVM, F2-F4 could be predicted accurately in 96%. Our study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy.
    Journal of Medical Systems 11/2006; 30(5):389-94. · 1.78 Impact Factor
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    ABSTRACT: Abstract—CombNET-II was one of the first methods proposed for solving classification problems with large number,of categories. This paper proposes an extension to this model, named CombNET-III, which replaces the MLP branch networks with multiclass SVMs and introduces a new probabilistic framework. The experiments show that CombNET-III outperforms both CombNET-II and a single multiclass SVM. Keywords— large scale classification problems,
    IEICE Transactions on Information and Systems 09/2006; 89-D:2533-2541. · 0.22 Impact Factor
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    ABSTRACT: The linear gating classifier (stem network) of the large scale model CombNET-II has been always the limiting factor which restricts the number of the expert classifiers (branch networks). The linear boundaries between its clusters cause a rapid decrease in the performance with increas- ing number of clusters and, consequently, impair the overall performance. This work proposes the use of a non-linear classifier to learn the complex boundaries between the clusters, which increases the gating performance while keeping the balanced split of samples produced by the original se- quential clustering algorithm. The experiments have shown that, for some problems, the proposed model outperforms the monolithic classifier.
    ESANN 2006, 14th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 26-28, 2006, Proceedings; 01/2006
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    Susumu Kuroyanagi, Akira Iwata
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    ABSTRACT: The purpose of this study is to construct an auditory information processing system using the pulsed neuron model, which is suited to hardware implementation. The realization of a competitive learning network composed only of pulsed neuron models is investigated. A new winner neuron decision process is proposed in which the threshold of each neuron is controlled by a state-detection neuron, which monitors the firing situation of the competitive learning neurons. A competitive learning network is constructed on the basis of the proposed method. An operation simulation demonstrates that the winner neuron can be determined by the proposed method, following the change of the input signal, by using only pulsed neuron models, thus realizing vector quantization suited to auditory information processing. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(13): 13–22, 2005; Published online in Wiley InterScience (). DOI 10.1002/scj.20346
    Systems and Computers in Japan 11/2005; 36:13-22.
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    ABSTRACT: Effective methods to reduce number of message cycles of adopt algorithm which is a complete algorithm for dis- tributed constraint optimization problems are proposed. The Adopt algorithm can perform branch and bound search asynchronously. However, it has overhead in backtracking and searches same partial solution repeatedly. To reduce overhead of backtracking, lower and upper bound of cost of partial solution are considered and some messages are sent to upper nodes by shortcut. Leaning of the lower and upper bound is used to reduce extra search. The results show the efficiency of the proposed methods. First, we illustrate DCOP and Adopt algorithm (7) in brief. In this paper, some notation and rules are modified. The DCOP as following is considered. A node (agent) i has a variable xi. xi has a value of di ∈ Di. Variables are related by binary constraint. Let xj denote a vari- able of node j which is related to xi. Cost of the value pair ((xi, di), (xj, dj)) is evaluated by fij(di, dj) which is the cost function of the constraint. Each node knows the constraints and cost functions which are related to their own variable. Each node selects the value of its own vari- able. In the following, node i and variable xi can be used interchangeably. Cost of a solution is a conjunction of fij(di, dj) for all constraints. Optimal solution has the minimum cost. Each node has message communication links to another nodes. Order of messages in a link is not changed. However, order of messages between links is not kept.
    IASTED International Conference on Artificial Intelligence and Applications, part of the 23rd Multi-Conference on Applied Informatics, Innsbruck, Austria, February 14-16, 2005; 01/2005
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    ABSTRACT: In a layered neural network, the error backpropagation method is generally used as the supervised learning procedure for the hidden layer, which cannot be observed from the outside. In order to apply the method, however, the output function of the neuron model must be differentiable. This paper proposes a supervised learning method for the hidden layer neurons in which the teaching signal is calculated for the hidden layer neurons, by utilizing the learning rule for the connection weight and the duality in the output layer neuron. The method is applicable so long as the neuron model contains duality, and it does not require that the output layer neurons or the hidden layer neurons be differentiable. As an example of a case in which the error backpropagation cannot be applied, a perceptron composed of neurons with a step output function is considered. The proposed method is applied, and the learning rule for the whole network is constructed. The XOR problem was actually learned by the network, and the same learning success rate was obtained as in the error backpropagation method for a perceptron composed of neurons with a sigmoid output function. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(9): 34–42, 2005; Published online in Wiley InterScience (). DOI 10.1002/scj.20206
    Systems and Computers in Japan 01/2005; 36:34-42.
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    ABSTRACT: The aim of this study is to develop a feature subset selection (FSS) method based on the margin of support vector machines (SVM). The problem of directly using the SVM margin is that it does not always provide clear relationship between its value and the performance of SVM, and the best obtained subset is not guaranteed to be the best possible one. In this paper, a new solution is describe by the introduction of the confident margin (CM) in the subset criterion, which permits to get near the best recognition rate by monitoring the peak of CM curve without directly calculating the recognition rate, in order to save computational time. The performance of the proposed method was evaluated in artificial and real-world data experiments.
    Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on; 01/2005
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    ABSTRACT: The Support Vector Machines (SVMs) had been showing a high capability of complex hyperplane representation and great generalization power. These characteristics lead to the development of more compact and less computational complex methods than the One-versus-Rest (OvR) and One-versus-One (OvO) [1] classical methods in the application of SVMs in multiclass problems. This paper proposes a new method for this task, named Truth Table Fitting Multiclass SVM (TTF-MCSVM), in which less SVMs are used than other classical methods. The main objective of this research is the development of an efficient method to be applied in problems with very large number of classes, like in the recognition of East Asian languages characters (e.g. Japanese and Chinese kanji).
    PRICAI 2004: Trends in Artificial Intelligence, 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004, Proceedings; 01/2004